diff --git a/.gitignore b/.gitignore
index f7d554ebe9..37e45b2de5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -78,4 +78,5 @@ MODULE.bazel.lock
 *.whl
 .coverage
 coverage.xml
-*.log
\ No newline at end of file
+*.log
+*.pt2
diff --git a/docsrc/user_guide/saving_models.rst b/docsrc/user_guide/saving_models.rst
index dc4b5da222..bef9b4dec3 100644
--- a/docsrc/user_guide/saving_models.rst
+++ b/docsrc/user_guide/saving_models.rst
@@ -14,12 +14,13 @@ Saving models compiled with Torch-TensorRT can be done using `torch_tensorrt.sav
 Dynamo IR
 -------------
 
-The output type of `ir=dynamo` compilation of Torch-TensorRT is `torch.fx.GraphModule` object by default. 
-We can save this object in either `TorchScript` (`torch.jit.ScriptModule`) or `ExportedProgram` (`torch.export.ExportedProgram`) formats by 
+The output type of `ir=dynamo` compilation of Torch-TensorRT is `torch.fx.GraphModule` object by default.
+We can save this object in either `TorchScript` (`torch.jit.ScriptModule`), `ExportedProgram` (`torch.export.ExportedProgram`) or `PT2` formats by
 specifying the `output_format` flag. Here are the options `output_format` will accept
 
 * `exported_program` : This is the default. We perform transformations on the graphmodule first and use `torch.export.save` to save the module.
 * `torchscript` : We trace the graphmodule via `torch.jit.trace` and save it via `torch.jit.save`.
+* `PT2 Format` : This is a next generation runtime for PyTorch models, allowing them to run in Python and in C++
 
 a) ExportedProgram
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -52,8 +53,8 @@ b) Torchscript
     model = MyModel().eval().cuda()
     inputs = [torch.randn((1, 3, 224, 224)).cuda()]
     # trt_gm is a torch.fx.GraphModule object
-    trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs)
-    torch_tensorrt.save(trt_gm, "trt.ts", output_format="torchscript", inputs=inputs)
+    trt_gm = torch_tensorrt.compile(model, ir="dynamo", arg_inputs=inputs)
+    torch_tensorrt.save(trt_gm, "trt.ts", output_format="torchscript", arg_inputs=inputs)
 
     # Later, you can load it and run inference
     model = torch.jit.load("trt.ts").cuda()
@@ -73,7 +74,7 @@ For `ir=ts`, this behavior stays the same in 2.X versions as well.
 
   model = MyModel().eval().cuda()
   inputs = [torch.randn((1, 3, 224, 224)).cuda()]
-  trt_ts = torch_tensorrt.compile(model, ir="ts", inputs=inputs) # Output is a ScriptModule object
+  trt_ts = torch_tensorrt.compile(model, ir="ts", arg_inputs=inputs) # Output is a ScriptModule object
   torch.jit.save(trt_ts, "trt_model.ts")
 
   # Later, you can load it and run inference
@@ -98,3 +99,26 @@ Here's an example usage
     inputs = [torch.randn((1, 3, 224, 224)).cuda()]
     model = torch_tensorrt.load(<file_path>).module()
     model(*inputs)
+
+b) PT2 Format
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+PT2 is a new format that allows models to be run outside of Python in the future. It utilizes `AOTInductor <https://docs.pytorch.org/docs/main/torch.compiler_aot_inductor.html>`_
+to generate kernels for components that will not be run in TensorRT.
+
+Here's an example on how to save and load Torch-TensorRT Module using AOTInductor in Python
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    model = MyModel().eval().cuda()
+    inputs = [torch.randn((1, 3, 224, 224)).cuda()]
+    # trt_ep is a torch.fx.GraphModule object
+    trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs)
+    torch_tensorrt.save(trt_gm, "trt.pt2", arg_inputs=inputs, output_format="aot_inductor", retrace=True)
+
+    # Later, you can load it and run inference
+    model = torch._inductor.aoti_load_package("trt.pt2")
+    model(*inputs)
diff --git a/examples/torchtrt_runtime_example/Makefile b/examples/torchtrt_runtime_example/Makefile
index 07fcc4c291..9adcad4757 100644
--- a/examples/torchtrt_runtime_example/Makefile
+++ b/examples/torchtrt_runtime_example/Makefile
@@ -1,14 +1,19 @@
 CXX=g++
 DEP_DIR=$(PWD)/deps
-INCLUDE_DIRS=-I$(DEP_DIR)/libtorch/include -I$(DEP_DIR)/torch_tensorrt/include
-LIB_DIRS=-L$(DEP_DIR)/torch_tensorrt/lib -L$(DEP_DIR)/libtorch/lib # -Wl,-rpath $(DEP_DIR)/tensorrt/lib
-LIBS=-Wl,--no-as-needed -ltorchtrt_runtime -Wl,--as-needed -ltorch -ltorch_cuda -ltorch_cpu -ltorch_global_deps -lbackend_with_compiler -lc10 -lc10_cuda
+CUDA_HOME=/usr/local/cuda
+INCLUDE_DIRS=-I$(DEP_DIR)/libtorch/include -I$(DEP_DIR)/torch_tensorrt/include -I$(CUDA_HOME)/include -I$(DEP_DIR)/libtorch/include/torch/csrc/api/include
+LIB_DIRS=-L$(DEP_DIR)/torch_tensorrt/lib -L$(DEP_DIR)/libtorch/lib -Wl,-rpath $(DEP_DIR)/tensorrt/lib
+LIBS=-Wl,--no-as-needed -ltorchtrt_runtime -ltorchtrt_plugins  -Wl,--as-needed -ltorch -ltorch_cuda -ltorch_cpu -ltorch_global_deps -lbackend_with_compiler -lc10 -lc10_cuda
 SRCS=main.cpp
 
 TARGET=torchtrt_runtime_example
 
 $(TARGET):
 	$(CXX) $(SRCS) $(INCLUDE_DIRS) $(LIB_DIRS) $(LIBS) -o $(TARGET)
+	echo "Add to LD_LIBRARY_PATH: $(DEP_DIR)/torch_tensorrt/lib:$(DEP_DIR)/libtorch/lib:$(DEP_DIR)/tensorrt/lib:$(CUDA_HOME)/lib64"
+
+generate_pt2:
+	uv run network.py
 
 clean:
 	$(RM) $(TARGET)
diff --git a/examples/torchtrt_runtime_example/main.cpp b/examples/torchtrt_runtime_example/main.cpp
index 0eff6fb638..a9967ef07e 100644
--- a/examples/torchtrt_runtime_example/main.cpp
+++ b/examples/torchtrt_runtime_example/main.cpp
@@ -3,31 +3,63 @@
 #include <memory>
 #include <sstream>
 #include <vector>
-#include "torch/script.h"
+#include "torch/torch.h"
+#include "torch/csrc/inductor/aoti_package/model_package_loader.h"
+#include "torch/csrc/inductor/aoti_runner/model_container_runner_cuda.h"
+
+/*
+ * This example demonstrates how to load and run a pre-built Torch-TensorRT
+ * AOTInductor (AOTI) model package using the PyTorch C++ API.
+ *
+ * Usage:
+ *   torchtrt_runtime_example <path-to-pre-built-trt-aoti module>
+ *
+ * Steps:
+ *   1. Parse the path to the AOTI model package from the command line.
+ *   2. Load the model package using AOTIModelPackageLoader.
+ *   3. Prepare a random CUDA tensor as input.
+ *   4. Run inference using the loaded model.
+ *   5. Print the output tensor(s) or an error message if inference fails.
+ */
 
 int main(int argc, const char* argv[]) {
+  // Check for correct number of command-line arguments
   if (argc < 2) {
-    std::cerr << "usage: samplertapp <path-to-pre-built-trt-ts module>\n";
+    std::cerr << "usage: torchtrt_runtime_example <path-to-pre-built-trt-aoti module>\n";
     return -1;
   }
 
-  std::string trt_ts_module_path = argv[1];
+  // Get the path to the TRT AOTI model package from the command line
+  std::string trt_aoti_module_path = argv[1];
 
-  torch::jit::Module trt_ts_mod;
+  // Enable inference mode for thread-local optimizations
+  c10::InferenceMode mode;
   try {
-    // Deserialize the ScriptModule from a file using torch::jit::load().
-    trt_ts_mod = torch::jit::load(trt_ts_module_path);
+      // Load the AOTI model package
+      torch::inductor::AOTIModelPackageLoader runner(trt_aoti_module_path);
+
+      // Create a random input tensor on CUDA with shape [1, 3, 5, 5] and type float32
+      std::vector<at::Tensor> inputs = {at::randn({1, 3, 5, 5}, {at::kCUDA}).to(torch::kFloat32)};
+
+      // Run inference using the loaded model
+      std::vector<at::Tensor> outputs = runner.run(inputs);
+
+      // Process and print the output tensor(s)
+      if (!outputs.empty()) {
+          std::cout << "Model output: " << outputs[0] << std::endl;
+      } else {
+          std::cerr << "No output tensors received!" << std::endl;
+      }
+
   } catch (const c10::Error& e) {
-    std::cerr << "error loading the model from : " << trt_ts_module_path << std::endl;
-    return -1;
+      // Handle errors from the PyTorch C++ API
+      std::cerr << "Error running model: " << e.what() << std::endl;
+      return 1;
+  } catch (const std::exception& e) {
+      // Handle other standard exceptions
+      std::cerr << "An unexpected error occurred: " << e.what() << std::endl;
+      return 1;
   }
 
-  std::cout << "Running TRT engine" << std::endl;
-  std::vector<torch::jit::IValue> trt_inputs_ivalues;
-  trt_inputs_ivalues.push_back(at::randint(-5, 5, {1, 3, 5, 5}, {at::kCUDA}).to(torch::kFloat32));
-  torch::jit::IValue trt_results_ivalues = trt_ts_mod.forward(trt_inputs_ivalues);
-  std::cout << "==================TRT outputs================" << std::endl;
-  std::cout << trt_results_ivalues << std::endl;
-  std::cout << "=============================================" << std::endl;
-  std::cout << "TRT engine execution completed. " << std::endl;
+  return 0;
 }
diff --git a/examples/torchtrt_runtime_example/network.py b/examples/torchtrt_runtime_example/network.py
index 5eac6399bc..89476f515d 100644
--- a/examples/torchtrt_runtime_example/network.py
+++ b/examples/torchtrt_runtime_example/network.py
@@ -1,6 +1,6 @@
 import torch
 import torch.nn as nn
-import torch_tensorrt as torchtrt
+import torch_tensorrt
 
 
 # create a simple norm layer.
@@ -29,21 +29,41 @@ def forward(self, x):
 
 def main():
     model = ConvGelu().eval().cuda()
-    scripted_model = torch.jit.script(model)
-
+    torch_ex_input = torch.randn([1, 3, 5, 5], device="cuda")
     compile_settings = {
-        "inputs": [torchtrt.Input([1, 3, 5, 5])],
+        "arg_inputs": [torch_ex_input],
+        "ir": "dynamo",
         "enabled_precisions": {torch.float32},
+        "min_block_size": 1,
     }
 
-    trt_ts_module = torchtrt.compile(scripted_model, **compile_settings)
-    torch.jit.save(trt_ts_module, "conv_gelu.jit")
+    cg_trt_module = torch_tensorrt.compile(model, **compile_settings)
+    torch_tensorrt.save(
+        cg_trt_module,
+        file_path="torchtrt_aoti_conv_gelu.pt2",
+        output_format="aot_inductor",
+        retrace=True,
+        arg_inputs=[torch_ex_input],
+    )
 
     norm_model = Norm().eval().cuda()
-    norm_ts_module = torch.jit.script(norm_model)
-    norm_trt_ts = torchtrt.compile(norm_ts_module, **compile_settings)
-    torch.jit.save(norm_trt_ts, "norm.jit")
-    print("Generated Torchscript-TRT models.")
+    norm_trt_module = torch_tensorrt.compile(norm_model, **compile_settings)
+    torch_tensorrt.save(
+        norm_trt_module,
+        file_path="torchtrt_aoti_norm.pt2",
+        output_format="aot_inductor",
+        retrace=True,
+        arg_inputs=[torch_ex_input],
+    )
+    print("Generated TorchTRT-AOTI models.")
+
+    loaded_cg_trt_module = torch._inductor.aoti_load_package(
+        "torchtrt_aoti_conv_gelu.pt2"
+    )
+    loaded_norm_trt_module = torch._inductor.aoti_load_package("torchtrt_aoti_norm.pt2")
+    with torch.inference_mode():
+        print(loaded_cg_trt_module(torch_ex_input))
+        print(loaded_norm_trt_module(torch_ex_input))
 
 
 if __name__ == "__main__":
diff --git a/py/torch_tensorrt/_compile.py b/py/torch_tensorrt/_compile.py
index 6dbf52de86..4d3ab91e32 100644
--- a/py/torch_tensorrt/_compile.py
+++ b/py/torch_tensorrt/_compile.py
@@ -586,6 +586,7 @@ def save(
     kwarg_inputs: Optional[dict[str, Any]] = None,
     retrace: bool = False,
     pickle_protocol: int = 2,
+    **kwargs: Any,
 ) -> None:
     """
     Save the model to disk in the specified output format.
@@ -595,7 +596,7 @@ def save(
         inputs (torch.Tensor): Torch input tensors
         arg_inputs (Tuple[Any, ...]): Same as inputs. Alias for better understanding with kwarg_inputs.
         kwarg_inputs (dict[Any, ...]): Optional, kwarg inputs to the module forward function.
-        output_format (str): Format to save the model. Options include exported_program | torchscript.
+        output_format (str): Format to save the model. Options include exported_program | torchscript | aot_inductor.
         retrace (bool): When the module type is a fx.GraphModule, this option re-exports the graph using torch.export.export(strict=False) to save it.
                 This flag is experimental for now.
         pickle_protocol (int): The pickle protocol to use to save the model. Default is 2. Increase this to 4 or higher for large models
@@ -603,7 +604,7 @@ def save(
     if isinstance(module, CudaGraphsTorchTensorRTModule):
         module = module.compiled_module
     module_type = _parse_module_type(module)
-    accepted_formats = {"exported_program", "torchscript"}
+    accepted_formats = {"exported_program", "torchscript", "aot_inductor"}
     if arg_inputs is not None and not all(
         isinstance(input, torch.Tensor) for input in arg_inputs
     ):
@@ -634,9 +635,9 @@ def save(
             "Input model is of type nn.Module. Saving nn.Module directly is not supported. Supported model types torch.jit.ScriptModule | torch.fx.GraphModule | torch.export.ExportedProgram."
         )
     elif module_type == _ModuleType.ts:
-        if output_format == "exported_program":
+        if not all([output_format == f for f in ["exported_program", "aot_inductor"]]):
             raise ValueError(
-                "Provided model is a torch.jit.ScriptModule but the output_format specified is exported_program. Please verify the output_format"
+                "Provided model is a torch.jit.ScriptModule but the output_format specified is not torchscript. Other output formats are not supported"
             )
         else:
             if arg_inputs is not None:
@@ -654,7 +655,22 @@ def save(
                 logger.warning(
                     "Provided model is a torch.export.ExportedProgram, inputs or arg_inputs is not necessary during save, it uses the inputs or arg_inputs provided during export and compile"
                 )
-            torch.export.save(module, file_path)
+            if output_format == "exported_program":
+                torch.export.save(module, file_path, pickle_protocol=pickle_protocol)
+            elif output_format == "aot_inductor":
+                inductor_configs = {}
+                if "inductor_configs" in kwargs:
+                    inductor_configs = kwargs["inductor_configs"]
+
+                torch._inductor.aoti_compile_and_package(
+                    exp_program,
+                    inductor_configs=inductor_configs,
+                    package_path=file_path,
+                )
+            else:
+                raise RuntimeError(
+                    "Attempted to serialize an exported program with an unsupported format. Exported programs support exported_program and aot_inductor"
+                )
     elif module_type == _ModuleType.fx:
         # The module type is torch.fx.GraphModule
         if output_format == "torchscript":
@@ -671,9 +687,24 @@ def save(
                         "Provided model is a torch.fx.GraphModule and retrace is False, inputs or arg_inputs is not necessary during save."
                     )
                 exp_program = export(module)
-                torch.export.save(
-                    exp_program, file_path, pickle_protocol=pickle_protocol
-                )
+                if output_format == "exported_program":
+                    torch.export.save(
+                        exp_program, file_path, pickle_protocol=pickle_protocol
+                    )
+                elif output_format == "aot_inductor":
+                    inductor_configs = {}
+                    if "inductor_configs" in kwargs:
+                        inductor_configs = kwargs["inductor_configs"]
+
+                    torch._inductor.aoti_compile_and_package(
+                        exp_program,
+                        inductor_configs=inductor_configs,
+                        package_path=file_path,
+                    )
+                else:
+                    raise RuntimeError(
+                        "Attempted to serialize an exported program with an unsupported format. Exported programs support exported_program and aot_inductor"
+                    )
             else:
                 if arg_inputs is None:
                     raise ValueError(
@@ -685,6 +716,22 @@ def save(
                     kwargs=kwarg_inputs,
                     strict=False,
                 )
-                torch.export.save(
-                    exp_program, file_path, pickle_protocol=pickle_protocol
-                )
+
+                if output_format == "exported_program":
+                    torch.export.save(
+                        exp_program, file_path, pickle_protocol=pickle_protocol
+                    )
+                elif output_format == "aot_inductor":
+                    inductor_configs = {}
+                    if "inductor_configs" in kwargs:
+                        inductor_configs = kwargs["inductor_configs"]
+
+                    torch._inductor.aoti_compile_and_package(
+                        exp_program,
+                        inductor_configs=inductor_configs,
+                        package_path=file_path,
+                    )
+                else:
+                    raise RuntimeError(
+                        "Attempted to serialize an exported program with an unsupported format. Exported programs support exported_program and aot_inductor"
+                    )
diff --git a/pyproject.toml b/pyproject.toml
index c527db1fc6..2dbc1ebc68 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,13 +8,13 @@ requires = [
     "cffi>=1.15.1",
     "typing-extensions>=4.7.0",
     "future>=0.18.3",
-    "tensorrt-cu12>=10.11.0,<10.12.0; 'tegra' not in platform_release",
-    "tensorrt>=10.3.0,<10.4.0; 'tegra' in platform_release",
-    "torch>=2.8.0.dev,<2.9.0; 'tegra' not in platform_release",
-    "torch>=2.7.0,<2.8.0; 'tegra' in platform_release",
+    "tensorrt-cu12>=10.11.0,<10.12.0; platform_machine != 'aarch64' or (platform_machine == 'aarch64' and 'tegra' not in platform_release)",
+    "tensorrt-cu12>=10.3.0,<10.4.0; platform_machine == 'aarch64' and 'tegra' in platform_release",
+    "torch>=2.8.0.dev,<2.9.0; platform_machine != 'aarch64' or (platform_machine == 'aarch64' and 'tegra' not in platform_release)",
+    "torch>=2.7.0,<2.8.0; platform_machine == 'aarch64' and 'tegra' in platform_release",
     "pybind11==2.6.2",
-    "numpy; 'tegra' not in platform_release",
-    "numpy<2.0.0; 'tegra' in platform_release",
+    "numpy; platform_machine != 'aarch64' or (platform_machine == 'aarch64' and 'tegra' not in platform_release)",
+    "numpy<2.0.0; platform_machine == 'aarch64' and 'tegra' in platform_release",
     "sympy",
     "dllist",
 ]
@@ -59,20 +59,24 @@ keywords = [
     "inference",
 ]
 dependencies = [
-    "torch>=2.8.0.dev,<2.9.0; 'tegra' not in platform_release",
-    "torch>=2.7.0,<2.8.0; 'tegra' in platform_release",
+    "torch>=2.8.0.dev,<2.9.0; platform_machine != 'aarch64' or (platform_machine == 'aarch64' and 'tegra' not in platform_release)",
+    "torch>=2.7.0,<2.8.0; platform_machine == 'aarch64' and 'tegra' in platform_release",
 
-    "tensorrt>=10.11.0,<10.12.0; 'tegra' not in platform_release",
-    "tensorrt>=10.3.0,<10.4.0; 'tegra' in platform_release",
+    "tensorrt>=10.11.0,<10.12.0; platform_machine != 'aarch64' or (platform_machine == 'aarch64' and 'tegra' not in platform_release)",
+    "tensorrt-cu12>=10.11.0,<10.12.0; platform_machine != 'aarch64' or (platform_machine == 'aarch64' and 'tegra' not in platform_release)",
+    "tensorrt-cu12-bindings>=10.11.0,<10.12.0; platform_machine != 'aarch64' or (platform_machine == 'aarch64' and 'tegra' not in platform_release)",
+    "tensorrt-cu12-libs>=10.11.0,<10.12.0; platform_machine != 'aarch64' or (platform_machine == 'aarch64' and 'tegra' not in platform_release)",
+
+    "tensorrt>=10.3.0,<10.4.0;platform_machine == 'aarch64' and 'tegra' in platform_release",
+    "tensorrt-cu12>=10.3.0,<10.4.0; platform_machine == 'aarch64' and 'tegra' in platform_release",
+    "tensorrt-cu12-bindings>=10.3.0,<10.4.0; platform_machine == 'aarch64' and 'tegra' in platform_release",
+    "tensorrt-cu12-libs>=10.3.0,<10.4.0; platform_machine == 'aarch64' and 'tegra' in platform_release",
 
-    "tensorrt-cu12>=10.11.0,<10.12.0; 'tegra' not in platform_release",
-    "tensorrt-cu12-bindings>=10.11.0,<10.12.0; 'tegra' not in platform_release",
-    "tensorrt-cu12-libs>=10.11.0,<10.12.0; 'tegra' not in platform_release",
 
     "packaging>=23",
 
-    "numpy; 'tegra' not in platform_release",
-    "numpy<2.0.0; 'tegra' in platform_release",
+    "numpy; platform_machine != 'aarch64' or (platform_machine == 'aarch64' and 'tegra' not in platform_release)",
+    "numpy<2.0.0; platform_machine == 'aarch64' and 'tegra' in platform_release",
 
     "typing-extensions>=4.7.0",
     "dllist",
@@ -101,8 +105,7 @@ torchvision = [
     "torchvision",
 ] #Leaving torchvisions dependency unconstrained so uv can just install something that should work for the torch we have. TV's on PyT makes it hard to put version constrains in
 quantization = ["nvidia-modelopt[all]>=0.27.1"]
-monitoring-tools = ["rich>=13.7.1"]
-jupyter = ["rich[jupyter]>=13.7.1"]
+
 
 [project.urls]
 Homepage = "https://pytorch.org/tensorrt"
@@ -121,14 +124,25 @@ prerelease = "if-necessary-or-explicit"
 index-strategy = "unsafe-best-match"                                    # Needed for TRT-LLM
 
 [tool.uv.sources]
-torch = [{ index = "pytorch-nightly-cu128" }]
-torchvision = [{ index = "pytorch-nightly-cu128" }]
+torch = [
+    { index = "pytorch-nightly-cu128", marker = "platform_machine != 'aarch64' or (platform_machine == 'aarch64' and 'tegra' not in platform_release)" },
+    { index = "jetson-containers", marker = "platform_machine == 'aarch64' and 'tegra' in platform_release" },
+]
+torchvision = [
+    { index = "pytorch-nightly-cu128", marker = "platform_machine != 'aarch64' or (platform_machine == 'aarch64' and 'tegra' not in platform_release)" },
+    { index = "jetson-containers", marker = "platform_machine == 'aarch64' and 'tegra' in platform_release" },
+]
 
 [[tool.uv.index]]
 name = "pytorch-nightly-cu128"
 url = "https://download.pytorch.org/whl/nightly/cu128"
 explicit = false
 
+[[tool.uv.index]]
+name = "jetson-containers"
+url = "https://pypi.jetson-ai-lab.dev/jp6/cu126"
+explicit = false
+
 [[tool.uv.index]]
 name = "nvidia"
 url = "https://pypi.nvidia.com"
diff --git a/tests/py/dynamo/models/test_export_serde.py b/tests/py/dynamo/models/test_export_serde.py
index 52e5eefb63..553624cc61 100644
--- a/tests/py/dynamo/models/test_export_serde.py
+++ b/tests/py/dynamo/models/test_export_serde.py
@@ -596,3 +596,56 @@ def forward(self, x):
         cos_sim > COSINE_THRESHOLD,
         msg=f"test_save_load_ts TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
     )
+
+
+@pytest.mark.unit
+def test_save_load_aoti(ir, tmp_path):
+    """
+    This tests save/load API on the AOTI format
+    """
+
+    class MyModule(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv = torch.nn.Conv2d(3, 16, 3, stride=1, bias=True)
+            self.relu = torch.nn.ReLU()
+
+        def forward(self, x):
+            conv = self.conv(x)
+            relu = self.relu(conv)
+            mul = relu * 0.5
+            return mul
+
+    model = MyModule().eval().cuda()
+    input = torch.randn((1, 3, 224, 224)).to("cuda")
+
+    trt_gm = torchtrt.compile(
+        model,
+        ir=ir,
+        inputs=[input],
+        min_block_size=1,
+        cache_built_engines=False,
+        reuse_cached_engines=False,
+    )
+    assertions.assertTrue(
+        isinstance(trt_gm, torch.fx.GraphModule),
+        msg=f"test_save_load_ts output type does not match with torch.fx.GraphModule",
+    )
+    outputs_trt = trt_gm(input)
+    print(f"{tmp_path}/trt.pt2")
+    torchtrt.save(
+        trt_gm,
+        f"{tmp_path}/trt.pt2",
+        output_format="aot_inductor",
+        arg_inputs=[input],
+        retrace=True,
+    )
+
+    trt_ts_module = torch._inductor.aoti_load_package(f"{tmp_path}/trt.pt2")
+    outputs_trt_deser = trt_ts_module(input)
+
+    cos_sim = cosine_similarity(outputs_trt, outputs_trt_deser)
+    assertions.assertTrue(
+        cos_sim > COSINE_THRESHOLD,
+        msg=f"test_save_load_ts TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+    )
diff --git a/uv.lock b/uv.lock
index e908b7069a..28be5259b6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -161,15 +161,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
 ]
 
-[[package]]
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-version = "3.0.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7", size = 61978, upload-time = "2024-11-30T04:30:14.439Z" }
-wheels = [
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-]
-
 [[package]]
 name = "async-timeout"
 version = "5.0.1"
@@ -362,18 +353,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934", size = 46018, upload-time = "2021-06-11T10:22:42.561Z" },
 ]
 
-[[package]]
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-version = "0.2.2"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "traitlets", marker = "sys_platform == 'linux' or sys_platform == 'windows'" },
-]
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-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e6/75/49e5bfe642f71f272236b5b2d2691cf915a7283cc0ceda56357b61daa538/comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3", size = 7180, upload-time = "2024-03-12T16:53:39.226Z" },
-]
-
 [[package]]
 name = "cppimport"
 version = "22.8.2"
@@ -390,9 +369,9 @@ name = "cupy-cuda12x"
 version = "13.4.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "fastrlock", marker = "(python_full_version < '3.10' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform == 'linux') or (python_full_version < '3.10' and sys_platform == 'windows') or (python_full_version >= '3.12' and sys_platform == 'windows') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'windows') or ('tegra' not in platform_release and sys_platform == 'linux') or ('tegra' not in platform_release and sys_platform == 'windows')" },
-    { name = "numpy", version = "1.26.4", source = { registry = "https://download.pytorch.org/whl/nightly/cu128" }, marker = "(python_full_version >= '3.12' and 'tegra' in platform_release and sys_platform == 'linux') or (python_full_version >= '3.12' and 'tegra' in platform_release and sys_platform == 'windows') or (python_full_version < '3.10' and sys_platform == 'linux') or (python_full_version < '3.10' and sys_platform == 'windows') or (platform_machine != 'aarch64' and 'tegra' in platform_release and sys_platform == 'linux') or (platform_machine != 'aarch64' and 'tegra' in platform_release and sys_platform == 'windows')" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.10' and 'tegra' not in platform_release and sys_platform == 'linux') or (python_full_version >= '3.10' and 'tegra' not in platform_release and sys_platform == 'windows')" },
+    { name = "fastrlock", marker = "(python_full_version < '3.10' and sys_platform == 'windows') or (python_full_version >= '3.12' and sys_platform == 'windows') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'windows') or ('tegra' not in platform_release and sys_platform == 'windows')" },
+    { name = "numpy", version = "1.26.4", source = { registry = "https://download.pytorch.org/whl/nightly/cu128" }, marker = "(python_full_version < '3.10' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and 'tegra' in platform_release and sys_platform == 'windows') or (python_full_version < '3.10' and sys_platform == 'windows') or (platform_machine != 'aarch64' and 'tegra' in platform_release and sys_platform == 'linux') or (platform_machine != 'aarch64' and 'tegra' in platform_release and sys_platform == 'windows')" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.10' and platform_machine != 'aarch64' and 'tegra' not in platform_release and sys_platform == 'linux') or (python_full_version >= '3.10' and 'tegra' not in platform_release and sys_platform == 'windows')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/55/ba/2fd0bbaf59ca964fc519fc3d9c8dc67813c83f75b3728d455fd7f89e6f09/cupy_cuda12x-13.4.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:113a4f6b5e89d8e3f0cb150708fa8586fde5f682d2d5bf4703ad8dde66063a5e", size = 117383051, upload-time = "2025-03-21T07:24:28.751Z" },
@@ -432,15 +411,6 @@ wheels = [
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-name = "decorator"
-version = "5.2.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711, upload-time = "2025-02-24T04:41:34.073Z" }
-wheels = [
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-]
-
 [[package]]
 name = "diffusers"
 version = "0.33.1"
@@ -483,7 +453,7 @@ name = "exceptiongroup"
 version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "(python_full_version < '3.12' and sys_platform == 'linux') or (python_full_version < '3.12' and sys_platform == 'windows')" },
+    { name = "typing-extensions", marker = "(python_full_version < '3.11' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform == 'windows')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749, upload-time = "2025-05-10T17:42:51.123Z" }
 wheels = [
@@ -499,15 +469,6 @@ wheels = [
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-version = "2.2.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/91/50/a9d80c47ff289c611ff12e63f7c5d13942c65d68125160cefd768c73e6e4/executing-2.2.0.tar.gz", hash = "sha256:5d108c028108fe2551d1a7b2e8b713341e2cb4fc0aa7dcf966fa4327a5226755", size = 978693, upload-time = "2025-01-22T15:41:29.403Z" }
-wheels = [
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-]
-
 [[package]]
 name = "expecttest"
 version = "0.1.6"
@@ -556,10 +517,9 @@ wheels = [
 [[package]]
 name = "filelock"
 version = "3.18.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0a/10/c23352565a6544bdc5353e0b15fc1c563352101f30e24bf500207a54df9a/filelock-3.18.0.tar.gz", hash = "sha256:adbc88eabb99d2fec8c9c1b229b171f18afa655400173ddc653d5d01501fb9f2", size = 18075, upload-time = "2025-03-14T07:11:40.47Z" }
+source = { registry = "https://download.pytorch.org/whl/nightly/cu128" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/4d/36/2a115987e2d8c300a974597416d9de88f2444426de9571f4b59b2cca3acc/filelock-3.18.0-py3-none-any.whl", hash = "sha256:c401f4f8377c4464e6db25fff06205fd89bdd83b65eb0488ed1b160f780e21de", size = 16215, upload-time = "2025-03-14T07:11:39.145Z" },
+    { url = "https://download.pytorch.org/whl/nightly/filelock-3.18.0-py3-none-any.whl" },
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 [[package]]
@@ -744,131 +704,6 @@ wheels = [
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+    { url = "https://download.pytorch.org/whl/nightly/sympy-1.14.0-py3-none-any.whl" },
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@@ -2882,7 +2590,7 @@ dependencies = [
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+    { name = "networkx", version = "3.5", source = { registry = "https://download.pytorch.org/whl/nightly/cu128" }, marker = "(python_full_version >= '3.11' and sys_platform == 'linux') or (python_full_version >= '3.11' and sys_platform == 'windows')" },
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     { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
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@@ -2930,17 +2638,11 @@ dependencies = [
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     { name = "tensorrt-cu12-libs", marker = "('tegra' not in platform_release and sys_platform == 'linux') or ('tegra' not in platform_release and sys_platform == 'windows')" },
-    { name = "torch", marker = "sys_platform == 'linux' or sys_platform == 'windows'" },
+    { name = "torch", marker = "('tegra' not in platform_release and sys_platform == 'linux') or ('tegra' not in platform_release and sys_platform == 'windows')" },
     { name = "typing-extensions", marker = "sys_platform == 'linux' or sys_platform == 'windows'" },
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 [package.optional-dependencies]
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-]
-monitoring-tools = [
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@@ -2971,18 +2673,16 @@ requires-dist = [
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-    { name = "torch", specifier = ">=2.8.0.dev0,<2.9.0", index = "https://download.pytorch.org/whl/nightly/cu128" },
+    { name = "torch", marker = "'tegra' not in platform_release", specifier = ">=2.8.0.dev0,<2.9.0", index = "https://download.pytorch.org/whl/nightly/cu128" },
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     { name = "typing-extensions", specifier = ">=4.7.0" },
 ]
-provides-extras = ["torchvision", "quantization", "monitoring-tools", "jupyter"]
+provides-extras = ["torchvision", "quantization"]
 
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 [[package]]
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+source = { registry = "https://download.pytorch.org/whl/nightly/cu128" }
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+    { url = "https://download.pytorch.org/whl/nightly/typing_extensions-4.14.0-py3-none-any.whl" },
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